Particle analyzers, such as, flow cytometers and hematology analyzers, measure physical properties of particles in a biological sample. Exemplary hematology analyzers are available from a number of companies including Beckman Coulter Inc., Sysmex Corp., Abbott Laboratories Inc., Siemens AG, and Shenzhen Mindray Bio-Medical Electronics Co., Ltd. Exemplary flow cytometers are available from a number of companies including Beckman Coulter Inc. and Becton, Dickinson and Company. Exemplary physical property measurements performed by particle analyzers include electro-optical measurements.
Measurements of different physical properties of particles are stored as particle analysis data. Each measured physical property corresponds to a feature (or parameter) in the particle analysis data. In this way, when multiple features are involved, the particle analysis data can form a multidimensional feature space. Each feature is associated with a dimension of the multidimensional feature space. Data points in the multidimensional feature space correspond to the particles. In particular, the measured physical property values of a particle can serve as coordinates of the corresponding data point in a multidimensional feature space.
Particles in a biological particle population usually share similar physical properties. Accordingly, data points corresponding to particles in the same population often group into clusters in the multidimensional feature space. Clusters in a multidimensional feature space are multidimensional clusters. For example, clusters in a three-dimensional (3D) feature space are 3D clusters. Classifying particle clusters can help users analyze the biological sample. Problems are encountered, however, when classifying particle clusters in a multidimensional feature space. Classifying particle clusters directly based on population types can be difficult in the multidimensional feature space due to the complex statistical distribution of the particles and the number of dimensions involved.
Particle populations can also be classified in two-dimensional (2D) projections of the multidimensional feature space. A 2D projection can be obtained by selecting data measuring two features from the multidimensional feature space. 2D clusters in the 2D projection can then be associated with particle populations. However, because a 2D projection does not contain data that measures features other than the two selected features, it can cause inaccurate results in particle population classification. For example, some particle populations can have similar values in two features but different values in other features. In a 2D projection over the two features, particles in these populations can overlap. A population classification based on that 2D projection alone would be inaccurate.
Because each 2D projection only contains data associated with two features, it is possible that each 2D projection becomes an isolated source of information. Global information such as cross-relations can be lost. Cross-relations include relationships among 2D clusters in the 2D projections. Sometimes a particle population can be projected to different locations and shapes in different 2D projections. Without cross-relations, it is difficult to identify such 2D clusters as corresponding to the same particle populations.
The classification of data points into particular particle populations based on 2D projections can be even less accurate for abnormal biological samples. Particle populations in abnormal samples are often shifted from their expected locations. This can cause heavy particle overlapping in 2D projections making it even more difficult to classify the overlapped particles into different populations.